Could algorithms make more equitable decisions? No: algorithms may propagate and amplify biases—its not enough just to learn/optimize. Collaboration with other fields has both a language gap and a value gap. Technical Algorithmic Fairness fair scheduling, distributed computing, envy-freeness, cake cutting, stable matching, etc. Interpreting Individual Probabilities “I have n% probability of developing this thing” — what does this mean? what does it capture in the environment? Applications insurance: how does actuarial scenarios work for this case—it is, in those cases, context dependent satisfying fairness maybe mis-generalized: discrimination can be subtle for a given measure (do you know it when you see it?) Definitions of fairness, however, is important to characterize a system. Group Fairness Intuition: for a few protected groups S, make sure that your predictor “behaves similarly” on S as on a general population U “similarly” statistical parity: every prediction outcome i equally is as likely on S and U balance: similar false positive and false negative on both S and U calibration: prediction values are accurate on average on S and U these systems are all at odds with each other, and also are often at odds with the overall utility. subgroups trying to advertise a burger store to vegetarians isn’t going to work; so, fairness requires identifying subgroups s \subseteq S which are relevant to the task multi-group fairness: offering “fairness protection” to every large subset of the population that can be identified given the data and computation limitations—fair by exhaustively protecting every group